We have recently released a webmapping system to serve global consistent environmental and Earth science layers at spatial resolutions from 10 km to 250 m (hopefully also soon at 100 m). This is an Open Data system as majority of layers are distributed under the Open Data Commons Open Database License (ODbL) and/or Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA). Read more about LandGIS in: http://opengeohub.org/about-landgis. You can access the web app at: https://landgis.opengeohub.org The system currently (Jan 2019) serves about 300+ layers from relief and geology, to vegetation indices, climatic images, soil properties and classes and potential and actual vegetation. Complete overview of available layers is available at: https://github.com/Envirometrix/LandGISmaps In addition to the web-mapping app, data can be accessed using the: - Geonode installation at https://maps.opengeohub.org, - LandGIS REST API services at https://landgisapi.opengeohub.org, - LandGIS WCS at https://geoserver.opengeohub.org/landgisgeoserver/web/, A copy of all layers is also available via Zenodo.org i.e. via an unique URL. To access data at point locations best use the REST API. For example, to access monthly precipitations at a location X, Y you can use: https://landgisapi.opengeohub.org/query/point?lat=7.58033&lon=35.6561&coll=layers1km®ex=clm_precipitation_imerge.(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)_m_1km_s0..0cm_.*_v0.1.tif which returns a GeoJSON (table) with precipitation values in mm. To access values of LandGIS layers at multiple points you can use: curl -X POST --form "points=@test_points.geojson" --form "layer=pnv_fapar_proba.v.jul_d_1km_s0..0cm_2014..2017_v0.1.tif" https://landgisapi.opengeohub.org/query/points -o results.json where test_points.geojson is the input GeoJSON file containing coordinates of points. The multi-point access is currently limited to max 20 points, but we hope to increase this number gradually. More examples of how to construct spatial queries are available at: https://landgisapi.opengeohub.org In addition to the REST access, you can also access the LandGIS data using the Web Coverage Service (WCS) functionality of the Geoserver e.g. to subset layers using a bounding box. For example, to download surface temperature for July for an area of about 300 by 300 km you can use: https://geoserver.opengeohub.org/landgisgeoserver/ows?service=WCS&version=2.0.1& request=GetCoverage& coverageId=layers1km:clm_lst_mod11a2.jul.day_m_1km_s0..0cm_2000..2017_v1.0& subset=Lat(41,45)&subset=Long(32,35) The read limit for WCS is 4GB and response size limit is 200MB. This means that WCS might fail if you try to fetch too large bounding boxes. If this happens we recommend instead downloading whole GeoTIFFs from Zenodo. We are currently preparing R functionality to allow users fetching data from LandGIS in a more systematic way (import, overlay, subset, plot...). If you would like to contribute to this initiative, especially to testing the R functions, please send me an email. Also we would appreciate if you would report any bug or inconsistency you discover via: https://github.com/Envirometrix/LandGISmaps/issues If you are currently producing any similar types of data (e.g. environmental layers at resolutions from 100 m to 1 km for a global land mask) and if you would like to publish this data on LandGIS, please forward a proposal for publishing your global layers: https://opengeohub.org/submitting-global-layers-inclusion-landgis and we will get on it asap. thank you, Tom Hengl https://opengeohub.org/people/tom-hengl
LandGIS Open Land data service - global stack of environmental layers
2 messages · Tomislav Hengl
2 days later
Just added an example with multipoint query to the LandGISmaps rep: https://github.com/Envirometrix/LandGISmaps/tree/master/tutorial REST now works for up to 50 points. The impressive speed is mainly thanks to using SSD and cloud GeoTIFF settings (https://github.com/Envirometrix/LandGISmaps#cloud-optimized-geotiff). library(rjson) library(rgdal) library(fossil) library(plotKML) ## REST multipoint query ---- path <- geopath(lon1=4.9, lon2=4.9, lat1=52.3, lat2=35.1) writeOGR(as(path, "SpatialPointsDataFrame"), "test_points.geojson", layer="test_points", driver="GeoJSON") ## overlay points and grids: browseURL('https://landgis.opengeohub.org/#/?base=Stamen%20(OpenStreetMap)¢er=49.6466,9.1126&zoom=7&opacity=80&layer=veg_fapar_proba.v.*_d&time=July') system('curl -X POST --form "points=@test_points.geojson" --form "layer=pnv_fapar_proba.v.jul_d_1km_s0..0cm_2014..2017_v0.1.tif" https://landgisapi.opengeohub.org/query/points -o results.json') df <- data.frame(matrix(unlist(rjson::fromJSON(file="results.json")), ncol = 3, byrow = TRUE)) str(df) plot(df[,2], df[,3], type="l") ## 255 is the missing value Let me know if you experience any problems. Tom Hengl https://opengeohub.org/people/tom-hengl
On 1/15/19 10:55 PM, Tomislav Hengl wrote:
We have recently released a webmapping system to serve global consistent environmental and Earth science layers at spatial resolutions from 10 km to 250 m (hopefully also soon at 100 m). This is an Open Data system as majority of layers are distributed under the Open Data Commons Open Database License (ODbL) and/or Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA). Read more about LandGIS in: http://opengeohub.org/about-landgis. You can access the web app at: https://landgis.opengeohub.org The system currently (Jan 2019) serves about 300+ layers from relief and geology, to vegetation indices, climatic images, soil properties and classes and potential and actual vegetation. Complete overview of available layers is available at: https://github.com/Envirometrix/LandGISmaps In addition to the web-mapping app, data can be accessed using the: - Geonode installation at https://maps.opengeohub.org, - LandGIS REST API services at https://landgisapi.opengeohub.org, - LandGIS WCS at https://geoserver.opengeohub.org/landgisgeoserver/web/, A copy of all layers is also available via Zenodo.org i.e. via an unique URL. To access data at point locations best use the REST API. For example, to access monthly precipitations at a location X, Y you can use: https://landgisapi.opengeohub.org/query/point?lat=7.58033&lon=35.6561&coll=layers1km®ex=clm_precipitation_imerge.(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)_m_1km_s0..0cm_.*_v0.1.tif which returns a GeoJSON (table) with precipitation values in mm. To access values of LandGIS layers at multiple points you can use: curl -X POST --form "points=@test_points.geojson" --form "layer=pnv_fapar_proba.v.jul_d_1km_s0..0cm_2014..2017_v0.1.tif" https://landgisapi.opengeohub.org/query/points -o results.json where test_points.geojson is the input GeoJSON file containing coordinates of points. The multi-point access is currently limited to max 20 points, but we hope to increase this number gradually. More examples of how to construct spatial queries are available at: https://landgisapi.opengeohub.org In addition to the REST access, you can also access the LandGIS data using the Web Coverage Service (WCS) functionality of the Geoserver e.g. to subset layers using a bounding box. For example, to download surface temperature for July for an area of about 300 by 300 km you can use: https://geoserver.opengeohub.org/landgisgeoserver/ows?service=WCS&version=2.0.1& request=GetCoverage& coverageId=layers1km:clm_lst_mod11a2.jul.day_m_1km_s0..0cm_2000..2017_v1.0& subset=Lat(41,45)&subset=Long(32,35) The read limit for WCS is 4GB and response size limit is 200MB. This means that WCS might fail if you try to fetch too large bounding boxes. If this happens we recommend instead downloading whole GeoTIFFs from Zenodo. We are currently preparing R functionality to allow users fetching data from LandGIS in a more systematic way (import, overlay, subset, plot...). If you would like to contribute to this initiative, especially to testing the R functions, please send me an email. Also we would appreciate if you would report any bug or inconsistency you discover via: https://github.com/Envirometrix/LandGISmaps/issues If you are currently producing any similar types of data (e.g. environmental layers at resolutions from 100 m to 1 km for a global land mask) and if you would like to publish this data on LandGIS, please forward a proposal for publishing your global layers: https://opengeohub.org/submitting-global-layers-inclusion-landgis and we will get on it asap. thank you, Tom Hengl https://opengeohub.org/people/tom-hengl